Data from sensors such as radars and sonars are sampled in polar or spherical coordinate systems, i.e. the sensors are scanning the surroundings with an angularly displaced beam. In the past, the received data were also displayed in polar coordinates on Plan Position Indicators using a rotating beam on a cathode ray tube. However, most modern display systems are raster scan devices, which operate in Cartesian coordinate system. The sensor data must therefore be converted into Cartesian coordinates. The conversion is a very processing intensive process, and this is in particular true for sensor systems on a moving mobile platform.
Prior art systems for coordinate transformation are based on one of the following techniques:                Direct mapping of (r,phi) to (x,y) coordinates by using “on the fly” calculations or by use of tables.        Use of inverse mapping of (x,y) to (r,phi) coordinates by using on the fly calculations or by use of tables.        Use of inverse mapping of (x,y) to (r,phi) coordinates by use of special circuits such as Pythagoras processors (ex. Plessey Semiconductor PDSP16330).        
The direct mapping approach will miss many pixels in the display system because the mapping is neither a one-to-one nor an onto-mapping. Some interpolation or “spoke filling” is therefore necessary.
The inverse mapping approach will avoid the interpolation problem, but would also be more computer-intensive in the mapping calculations. Using tables will require recalculation of a new mapping table every time the area of display is moved or scaled. In mobile sensor scenarios where the display area is geographically fixed this can require time consuming recalculations of the tables. Hence, this is not an optimal solution for a system needing real-time performance.